So-called Natural Language Understanding deals with the task of making human language processable for the computer and recording the content for other tasks, virtually understanding the content. This article deals with the concepts of natural language understanding, the difference with NLP and which applications can be implemented with it.
What is natural language understanding?
Natural Language Understanding, or NLU for short, covers a part of artificial intelligence, which is concerned with making natural language understandable in terms of content for computers. This makes it possible to implement very complex applications, such as summarizing texts. The complexity of NLU consists in making the language understandable to the computer in all its facets, such as synonyms or irony.
What appears simple to us humans is highly complex for the machine, because on the one hand, our brain associates words with memories, images and feelings and places the words in the right context. Computer software must first learn this knowledge and replace it with other tools. In addition, the software must convert the texts into machine-understandable components, i.e. long columns of numbers.
What do NLP and NLU have to do with each other?
Natural Language Understanding (NLU) and Natural language processing (NLP) are often confused or mistakenly used as synonyms. It is therefore interesting to go into more detail about how these areas are interrelated. For a deeper introduction to natural language processing, this lexicon also has its own Articles about NLP. However, the basic points are also repeated in this post.
NLP is a subject area that deals with the processing of text through software. As an independent subject area, it is also comparatively old and has been in operation since the 1950s. Using methods from artificial intelligence, linguistics and computer science, it attempts to process human language in written and spoken form. In addition to understanding existing texts, NLP also includes the creation of new text data.
Possible applications for Natural Language Support (NLU):
- voice assistants
- Entity recognition, e.g. people or places, for chatbots
- Automatic translation
- spam filter
- Language analysis for market research
Possible applications for Natural Language Generation (NLG):
- Create personalized emails
- content marketing
- Real time text generation in games
- Preparation of product descriptions
- preparation of text summaries
Natural Language Understanding (NLU) deals with the actual understanding of the processed text and includes tasks such as examining semantics and syntax or determining the emotional state in which the text was written. NLU is therefore one of many topics in the area of NLP.
Natural language generation, on the other hand, allows computers to generate their own text, which is created based on input. In addition, this area also includes the ability to summarize entire texts and only reproduce the main points. However, this point overlaps with NLU, as the text must first be understood before it can be summarized.
How does Natural Language Understanding work?
Natural language understanding often involves working with unstructured text data that does not have a fixed structure, as is the case with a table, for example. Such data makes it difficult to retrieve specific information. For example, to query names from a table, all you have to do is read out the corresponding column, but in the case of text data, the names can be in different places, making it much more difficult to extract all names from the text. These are usually large, coherent text passages that cannot be processed directly by the computer in this way. There are now various ways in which such preprocessing can take place. Through the development of better and more powerful models in the field of artificial intelligence, various paths have emerged, which also depend on the application. Previous NLU models focused on the following points, for example:
- Tokenization: The text is divided into so-called tokens. These can be words, phrases, or even just individual syllables. With the help of tokens, a certain statement can then be made about the content of the text, for example by counting frequently occurring words. In addition, filler words can be removed that have no major effect on the content of the text, such as articles such as “a” or “that.” After the so-called “stop words” have been removed, the computer can focus on the actual content.
- Syntax analysis: After the individual words have been recognized, a grammatical analysis is carried out to understand the structure of the sentences. The technology used here is so-called Part-of-Speech Tagging, or POS tagging for short. This recognizes and marks nouns, verbs, and adjectives, for example, which allows the words to be linked together, which contributes to further understanding.
- semantic analysis: This final step attempts to understand the content of the text and its meaning. The semantic analysis is based on the previous processing steps. One method in this area is the so-called Named Entity Recognition (NER), in which entities, such as names, places or organizations, are recognized and marked in the text. As a result, relevant information can be extracted and connections can be formed. In addition, the “sentiment” of the text can be found out, i.e. whether the text was written in a friendly, neutral or negative tone.
Large language models are currently increasingly being used for natural language processing, which no longer requires such detailed preparatory work.
What is the difference between NLU and LLMs?
NLU and LLMs are two abbreviations that are commonly used in the area of machine learning. The NLU stands for Natural Language Understanding and comprises one of many sub-areas of artificial intelligence. The aim is to create computer software that is able to understand and process human language. This technology is used in various applications, such as spam filters or voice assistants.
Large language models, LLMs for short, on the other hand, form a class of machine learning models that use large amounts of text data to try to recognize linguistic structures and patterns and thus learn natural language. The most well-known representative of LLMs is the Generative Pretrained Transformer, GPT for short, which is the basis for the ChatGPT application and can be used in various variants.
LLMs are therefore a potential technology that is used to implement natural language understanding.
Which applications use NLU?
The automated understanding of natural language is used in many areas where large amounts of text are to be processed automatically.
voice assistants, such as Alexa or Siri, allow spoken commands from devices to be understood and executed. This allows you to switch off the lights in the smart home or play the next song. The underlying models must be able to understand the spoken language and also various dialects or modes of expression.
Companies and authorities in particular rely on chatbots, which make it possible to either process written user inquiries live directly or forward them to a correct contact person. As a result, the user can be helped quickly and the workload of specialists can be reduced. The user's inputs are processed and, for example, specific entities are extracted, such as information about the person and location, which is required in the course of the request. In addition, the input is used and a so-called intent analysis is started, which finds out what the request is about. For a deep dive into chatbots, their basics and application, you can use, among other things, our detailed e-book on this topic, which here can be downloaded.
The NLU is also used for so-called Sentiment analyses used, which classify product reviews or social media comments based on their sentiment. This is intended to identify whether the reviews are more positive or negative. With the help of such analyses, the large number of comments can be processed easily and quickly. Models that are used for sentiment analyses must be able to understand the peculiarities of social media and, on top of that, be able to correctly interpret ironic or sarcastic statements.
Another area of application of NLU is its use within automatic translation. After all, a good translation is not only characterized by the fact that all words are correctly adopted into the new language, but also that contexts are addressed and therefore the appropriate translation of individual words or phrases is also used.
Stumbling blocks for NLU systems
Human language has many pitfalls that software systems in the NLU sector must deal with. These make it difficult to determine the actual content and context and must be taken into account when creating the models. The challenges include:
- Semantic diversity: Natural language is extremely diverse and depends heavily on context. For example, a word can have several meanings, depending on the context in which it is used or how it is pronounced. With the word case For example, it could be either a fall or a criminal case.
- demonstrative pronoun: Demonstrative pronouns refer to people or objects that were used in a previous context, e.g. “The dog next door is a puppy. This one has brown fur.” In order to correctly classify these pronouns, NLU models must have short-term memory so that the information from the previous sentence is still available when the word “this” is processed.
- Irony and sarcasm: The meaning of ironic or sarcastic statements is usually the opposite of what is actually said. On a rainy and cold day, in an ironic context, the statement “But the weather is particularly beautiful today.” could quickly be heard. The ironic meaning is then largely understandable only through the emphasis or context, and it is also sometimes difficult for us humans to recognize the ironic meaning. Accordingly, it is even more difficult for computer systems to understand the true content of an ironic or sarcastic statement.
- Dialects and slang: The use of language varies significantly not only between different regions, but also between social groups and age groups. More and more words are abbreviated or pronounced or written in a slightly different way. NLU systems must recognize and correctly interpret these nuances.
This is just a selection of peculiarities that almost every human language entails and must be learned by NLU systems. There are various approaches that can be used to achieve this. One option is to use texts from a wide variety of contexts and applications when training on large data sets. The Internet is often a good source for this, as it contains a variety of texts that can be colloquial or very technical. However, there are other problems here, such as the processing of very large amounts of data or the lack of content regulation, which can contain very one-sided opinions or simply incorrect information. Therefore, you should be careful when publishing today's chatbots and compare the information with independent sources whenever possible.
conclusion
Natural language understanding is an exciting part of artificial intelligence that is currently making a lot of progress and is being used in some applications, such as language systems or chatbots. It faces a number of challenges associated with human language, such as understanding irony or sarcasm or learning dialects.
In the future, research will focus on even more robust models that can achieve higher accuracies and can therefore better address details. Overall, Natural Language Understanding offers the opportunity for the interaction between humans and computers to become ever better and that human-computer interaction can fundamentally change.
